Method and apparatus for providing internet service in mobile communication terminal

ABSTRACT

A method and apparatus provide an Internet service in a mobile communication terminal. The method includes determining a user interest subject from user data existing within the mobile communication terminal, collecting service items through network access, determining a subject for each of the collected service items, determining relevance between the user interest subject and each of the service items, and recommending a service item according to the relevance.

CROSS-REFERENCE TO RELATED APPLICATION(S) AND CLAIM OF PRIORITY

The present application is related to and claims priority under 35U.S.C. §119(a) to a Korean Patent Application filed in the KoreanIntellectual Property Office on Sep. 10, 2010 and assigned Serial No.10-2010-0088709, the contents of which are herein incorporated byreference.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to a method and apparatus for providing anInternet service in a mobile communication terminal. More particularly,the present invention relates to a method and apparatus for analyzinguser data of a mobile communication terminal and recommending a servicein the mobile communication terminal.

BACKGROUND OF THE INVENTION

With the rapid development of mobile communication technologies, variousservices using mobile communication terminals are being provided.Particularly, in recent years, a service of providing information on auser interest field considering the user interest field in a mobilecommunication terminal, i.e., a personalized information service of anon-demand scheme is being provided.

FIG. 1 illustrates a method of providing a personalized informationservice of an on-demand scheme according to the conventional art.

As illustrated in FIG. 1, the conventional personalized informationservice is of a scheme in which, if a mobile communication terminal 100registers a user interest field to a user information storage server 102(operation 110), the user information storage server 102 provides theregistered user interest field to a personalized information provisionserver 104, and the personalized information provision server 104searches service information associated with the user interest field andthen provides the searched service information to the mobilecommunication terminal 100 (operation 112).

In the conventional personalized information service, a mobilecommunication terminal should previously register a user interest fieldto a server as above. That is, the conventional personalized informationservice has a disadvantage that, unless the mobile communicationterminal previously registers the user interest field to the server, themobile communication terminal cannot be provided with desiredinformation. Further, in the conventional personalized informationservice, whenever a user's own interest field changes, a user has toregister the changed interest field to the server himself/herself, sothere is a problem of causing troublesomeness and inconvenience at userside.

SUMMARY OF THE INVENTION

To address the above-discussed deficiencies of the prior art, it is aprimary object to provide at least the advantages below. Accordingly,one aspect of the present disclosure is to provide a method andapparatus for analyzing user data and recommending an Internet servicein a mobile communication terminal.

Another aspect of the present disclosure is to provide a method andapparatus for determining a user interest subject from user data,collecting Internet service items, determining a subject of the serviceitems, and then recommending a service item corresponding to the userinterest subject in a mobile communication terminal.

A further aspect of the present disclosure is to provide a method andapparatus for extracting term vectors from user data and each serviceitem and determining syntactic similarity between respective termvectors in a mobile communication terminal.

Yet another aspect of the present disclosure is to provide a method andapparatus for extracting subjects from user data and each service itemand determining semantic similarity between respective subjects in amobile communication terminal.

Still another aspect of the present disclosure is to provide a methodand apparatus for determining similarity between a user interest subjectand a service item subject, and recommending a service according to thesimilarity in a mobile communication terminal.

Still another aspect of the present disclosure is to provide a methodand apparatus for determining a term vector reflecting a feature of ahierarchical structure between categories, for each category of asubject classification tree in a mobile communication terminal.

Still another aspect of the present disclosure is to provide a methodand apparatus for determining relevance to other categories for eachcategory of a subject classification tree and, based on the relevance,recommending a service corresponding to a user interest subject in amobile communication terminal.

The above aspects are achieved by providing a method and apparatus forproviding an Internet service in a mobile communication terminal.

According to one aspect of the present disclosure, a method forproviding an Internet service in a mobile communication terminal isprovided. The method includes determining a user interest subject fromuser data existing within the mobile communication terminal, collectingservice items through network access, determining a subject for each ofthe collected service items, determining relevance between the userinterest subject and each of the service items, and recommending aservice item according to the relevance.

According to another aspect of the present disclosure, an apparatus forproviding an Internet service in a mobile communication terminal isprovided. The apparatus includes a user interest subject determinationunit, a service item collection and classification unit, a service itemranking unit, and a service recommendation unit. The user interestsubject determination unit determines a user interest subject from userdata existing within the mobile communication terminal. The service itemcollection and classification unit collects service items throughnetwork access, and determines a subject for each of the collectedservice items. The service item ranking unit determines relevancebetween the user interest subject and each of the service items. Theservice recommendation unit recommends a service item according to therelevance.

Other aspects, advantages and salient features of the disclosure willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses illustrative embodiments of the disclosure.

Before undertaking the DETAILED DESCRIPTION OF THE INVENTION below, itmay be advantageous to set forth definitions of certain words andphrases used throughout this patent document: the terms “include” and“comprise,” as well as derivatives thereof, mean inclusion withoutlimitation; the term “or,” is inclusive, meaning and/or; the phrases“associated with” and “associated therewith,” as well as derivativesthereof, may mean to include, be included within, interconnect with,contain, be contained within, connect to or with, couple to or with, becommunicable with, cooperate with, interleave, juxtapose, be proximateto, be bound to or with, have, have a property of, or the like; and theterm “controller” means any device, system or part thereof that controlsat least one operation, such a device may be implemented in hardware,firmware or software, or some combination of at least two of the same.It should be noted that the functionality associated with any particularcontroller may be centralized or distributed, whether locally orremotely. Definitions for certain words and phrases are providedthroughout this patent document, those of ordinary skill in the artshould understand that in many, if not most instances, such definitionsapply to prior, as well as future uses of such defined words andphrases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and itsadvantages, reference is now made to the following description taken inconjunction with the accompanying drawings, in which like referencenumerals represent like parts:

FIG. 1 is a diagram illustrating a method of providing a personalizedinformation service of an on-demand scheme according to the conventionalart;

FIG. 2 is a block diagram illustrating a construction of a mobilecommunication terminal for providing a personalized information serviceaccording to the present disclosure;

FIG. 3 is a block diagram illustrating a detailed construction of a userinterest subject determination unit in a mobile communication terminalaccording to an embodiment of the present disclosure;

FIG. 4 is a block diagram illustrating a detailed construction of aservice item collection and classification unit in a mobilecommunication terminal according to an embodiment of the presentdisclosure;

FIG. 5 is a block diagram illustrating a detailed construction of aservice item ranking unit in a mobile communication terminal accordingto an embodiment of the present disclosure;

FIG. 6 is a diagram illustrating user data in a mobile communicationterminal according to an embodiment of the present disclosure;

FIG. 7 is a diagram illustrating an eXtensible Markup Language (XML)file generated in a mobile communication terminal according to anembodiment of the present disclosure;

FIG. 8 is a diagram illustrating a user interest subject determinedbased on user data in a mobile communication terminal according to anembodiment of the present disclosure;

FIG. 9 is a diagram illustrating an example of an Internet service itemcollected in a mobile communication terminal according to an embodimentof the present disclosure;

FIG. 10 is a diagram illustrating similarity between service itemsubjects in a mobile communication terminal according to an embodimentof the present disclosure;

FIG. 11 is a diagram illustrating a method for obtaining a ‘tw_(ij)’value when following the second circulation pattern in an embodiment ofthe present disclosure;

FIG. 12 is a diagram illustrating a screen construction of recommendinga service item in a mobile communication terminal according to anembodiment of the present disclosure; and

FIGS. 13A and 13B illustrate a procedure of recommending a service itemaccording to a user interest subject in a mobile communication terminalaccording to an embodiment of the present disclosure.

Throughout the drawings, like reference numerals will be understood torefer to like parts, components and structures.

DETAILED DESCRIPTION OF THE INVENTION

FIGS. 2 to 13B, discussed below, and the various embodiments used todescribe the principles of the present disclosure in this patentdocument are by way of illustration only and should not be construed inany way to limit the scope of the disclosure. Those skilled in the artwill understand that the principles of the present disclosure may beimplemented in any suitably arranged electronic device. Preferredembodiments of the present invention will be described herein below withreference to the accompanying drawings. In the following description,well-known functions or constructions are not described in detail sincethey would obscure the invention in unnecessary detail. And, termsdescribed below, which are defined considering functions in the presentinvention, can be different depending on user and operator's intentionor practice. Therefore, the terms should be defined on the basis of thedisclosure throughout this specification

Below, illustrative embodiments of the present disclosure provide amethod and apparatus for analyzing user data and recommending a servicein a mobile communication terminal.

FIG. 2 illustrates a construction of a mobile communication terminal forproviding a personalized information service according to the presentdisclosure.

Referring to FIG. 2, the mobile communication terminal 200 includes auser interest subject determination unit 210, a service item collectionand classification unit 220, a service item ranking unit 230, and apersonalized service recommendation unit 240.

The user interest subject determination unit 210 analyzes user dataexisting within the mobile communication terminal and determines a userinterest subject. Here, the user data means data such as a short messageexisting within the mobile communication terminal, a multimedia message,an electronic-mail (e-mail), a file, a schedule, a memo, Web-usageinformation and the like. In detail, the user interest subjectdetermination unit 210 extracts a text from the user data existingwithin the mobile communication terminal, analyzes the text, generatesterm vectors, classifies the term vectors according to a subjectclassification tree embedded in the mobile communication terminal, anddetermines a user interest subject. Here, the subject classificationtree is classifying, by subject, concepts suitable for indicating userinterest fields and expressing the classified concepts in a treestructure. For example, the subject classification tree can be an opendirectory project widely known in the art. Undoubtedly, a general opendirectory project is of as much wide range as being a Web directory, sothe present disclosure may extract certain categories suitable forindicating user interest fields from the open directory project, foruse. The user interest subject determination unit 210 is described laterin detail with reference to FIG. 3.

The service item collection and classification unit 220 accesses theInternet 202, collects Internet service items, analyzes texts of thecollected service items, generates term vectors, classifies the termvectors according to a subject classification tree embedded in themobile communication terminal, and determines a subject of each of thecollected service items. The service item collection and classificationunit 220 is described later in detail with reference to FIG. 4.

The service item ranking unit 230 determines syntactic similarity andsemantic similarity using the term vector and user interest subject forthe user data and the term vector and service item subject for theservice items, determines the total similarity between the user interestsubject and the service item subject using the syntactic similarity andsemantic similarity, and determines a relevance rank of each of theservice items to the user interest subject. The service item rankingunit 230 is described later in detail with reference to FIG. 5.

The personalized service recommendation unit 240 controls a functionfor, if a service item recommendation event occurs, displaying a windowfor selecting the kind of recommendation service item through a screenand, if the kind of recommendation service item is selected, determiningservice items corresponding to the selected kind in consideration of arelevance rank determined in the service item ranking unit 230 anddisplaying a list including the determined service items on the screen.Further, the personalized service recommendation unit 240 controls andprocesses a function for, if any one of the recommendation service itemsis selected, displaying the detailed contents of the selected serviceitem on the screen. For example, as illustrated in FIG. 12, thepersonalized service recommendation unit 240 controls and processes afunction for displaying a window for selecting which kind of serviceitem will be recommended among news or mobile Applications (Apps),displaying a list including mobile App services of a great relevance toa user interest subject and, if the mobile App service is selected by auser, displaying detailed information on the mobile App service selectedby the user on the screen.

Thus, a detailed construction of the mobile communication terminal isdescribed below with reference to FIGS. 3 to 5.

FIG. 3 illustrates a detailed construction of a user interest subjectdetermination unit in a mobile communication terminal according, to anembodiment of the present disclosure.

Referring to FIG. 3, the user interest subject determination unit 210includes a user data text extractor 310, a user data text analyzer 320,and a user data term vector classifier 330.

The user data text extractor 310 extracts a text representing a userinterest subject from user data existing within the mobile communicationterminal. For instance, the user data text extractor 310 can extract atext from a short message, a multimedia message, an e-mail, a file, aschedule, a memo, Web-usage information and the like stored within themobile communication terminal as illustrated in FIG. 6. At this time,the user data text extractor 310 extracts a generation time andgeneration position of the extracted text, and metadata of acorresponding application together, and stores the extraction result inan Extensible Markup Language (XML) form. Here, the metadata of theapplication includes at least one of the kind of the application, afeature, a revision time, a generation time, and context information.Here, the context information can be used for applying a weight at thetime of generating a term vector in the user data text analyzer 320.That is, the user data text extractor 310 can extract a text from theuser data of FIG. 6 and, at this time, generate an XML file of FIG. 7.

The user data text analyzer 320 analyzes text data extracted from theuser data text extractor 310 and generates a term vector according to avector space model. Here, the term vector is composed of individualterms existing in the text data, and can be generated reflecting weightsdependent on the importance of respective terms within an extractedtext. At this time, the weights dependent on the importance of therespective terms can be determined considering the frequency within theextracted text, and a generation time and generation position of thetext. For instance, in an example where the frequency of appearance of aspecific term in texts provided from the user data text extractor 310 ishigh, the user data text analyzer 320 can determine the specific term asa key term expressing a user interest subject and set a high weight tothe specific term. Further, the user data text analyzer 320 can setweights to respective terms using a Term Frequency Inverse DocumentFrequency (TFIDF) weight allocation method widely known in the art, orcan set weights using context information recorded in an XML filegenerated in the user data text extractor 310. For instance, the userdata text analyzer 320 can set higher weights to more recently generatedterms based on the context information recorded in the XML file and,through this, can obtain an effect of being capable of reflecting arecent user interest subject.

If term vectors for respective terms within a text are generated in theuser data text analyzer 320, the user data term vector classifier 330classifies the generated term vectors based on a subject classificationtree 340 embedded in the mobile communication terminal and determines auser interest subject. Here, the subject classification tree 340classifies, by subject, concepts suitable for indicating user interestfields and expresses the classified concepts in a tree structure. Forexample, the subject classification tree 340 can be an open directoryproject known in the art. Undoubtedly, a general open directory projectknown in the art is of as much wide range as being a Web directory, sothe present disclosure may extract certain some categories suitable forindicating user interest fields from the open directory project, foruse. Here, each category of the subject classification tree 340 caninclude a list of Web pages corresponding to the each category. The listof Web pages may include terms representing a characteristic of acorresponding category.

The user data term vector classifier 330 can perform machine learningfor term vector classification with reference to the list of Web pagesincluded in each category of the subject classification tree 340. Atthis time, a machine learning algorithm can be Rocchio's algorithm,K-Nearest-Neighbor (KNN) algorithm, Naive Bayes (NB) algorithm, SupportVector Machine (SVM) algorithm, and the like widely known in the art.For instance, in a example where Web pages ‘a’, ‘b’, and ‘c’ areincluded in a category ‘A’, the user data term vector classifier 330 maybe learned to classify, as the category ‘A’, term vectors correspondingto the Web page ‘a’. After the learning is completed, if a user dataterm vector is input, the user data term vector classifier 330 candetermine a category corresponding to the user data term vector withreference to the subject classification tree 340, and determine asubject of the determined category as a user interest subjectcorresponding to the user data term vector. For instance, the user dataterm vector classifier 330 may classify term vectors extracted from theuser data of FIG. 6 and, as illustrated in FIG. 8, classify the termvectors into five categories and determine a subject of each category asa user interest subject.

FIG. 4 illustrates a detailed construction of a service item collectionand classification unit in a mobile communication terminal according toan embodiment of the present disclosure.

Referring to FIG. 4, the service item collection and classification unit220 includes a mobile Internet service item collector 410, a serviceitem text analyzer 420, and a service item term vector classifier 430.

The mobile Internet service item collector 410 accesses the mobileInternet 202 and collects service items (e.g., news and mobile Apps)recommendable to a user. For instance, the mobile Internet service itemcollector 410 collects the latest mobile App information from a mobileApp site suitable to an operation environment of the mobilecommunication terminal, and collects the latest news from a news portalsite enabling information collection. At this time, the mobile Internetservice item collector 410 can collect related service items using auser interest subject determined in the user interest subjectdetermination unit 210.

The service item text analyzer 420 extracts a text from service itemscollected in the mobile Internet service item collector 410, analyzesthe extracted text, and generates a term vector according to a vectorspace model. Here, the term vector is composed of individual termsexisting in the text of the collected service items, and reflectsweights dependent on the importance of respective terms within theextracted text. Here, the weights dependent on the importance ofrespective terms can be determined considering the frequency of eachterm within the extracted text and a generation time and generationposition of the text. That is, the service item text analyzer 420 mayset the weights dependent on the importance of the respective ten us inthe same method as that of the user data text analyzer 320.

The service item term vector classifier 430 classifies term vectorsgenerated in the service item text analyzer 420 based on a subjectclassification tree 440 embedded in the mobile communication terminal,and determines a subject of each of the collected service items. Here,the service item term vector classifier 430 classifies the term vectorsin the same method as that of the user data term vector classifier 330and determines a corresponding subject. Further, the subjectclassification tree 440 referred in the service item term vectorclassifier 430 is the same as the subject classification tree 340referred in the user data term vector classifier 330.

FIG. 5 illustrates a detailed construction of a service item rankingunit in a mobile communication terminal according to an embodiment ofthe present disclosure.

Referring to FIG. 5, the service item ranking unit 230 includes asyntactic matching unit 510, a semantic matching unit 520, and anintegration ranking unit 530.

The syntactic matching unit 510 determines syntactic similarity betweena term vector generated in the user data text analyzer 320 and a termvector generated in the service item text analyzer 420. By determiningthe cosine similarity of a vector space model according to Equation 1below, the syntactic matching unit 510 determines syntactic similaritybetween a term vector for user data and a term vector for service items.

Equation 1 below represents a formula of determining cosine similarity.

$\begin{matrix}{{{SyntactisS}\mspace{14mu}{core}\mspace{11mu}\left( {\overset{\rightarrow}{u},{\overset{\rightarrow}{s}}_{i}} \right)} = {{\cos\left( {\overset{\rightarrow}{u},{\overset{\rightarrow}{s}}_{i}} \right)} = \frac{\overset{\rightarrow}{u} \cdot {\overset{\rightarrow}{s}}_{i}}{{\overset{\rightarrow}{u}}{{\overset{\rightarrow}{s}}_{i}}}}} & (1)\end{matrix}$

Here, the ‘{right arrow over (u)}’ represents a term vector for userdata, and the ‘{right arrow over (s_(i))}’ represents a term vector forservice items.

The semantic matching unit 520 determines semantic similarity between auser interest subject extracted from the user data term vectorclassifier 330 and a service item subject determined in the service itemterm vector classifier 430. By applying a weighted Personalized PageRank(wPPR) algorithm to a similarity graph 540 representing semanticsimilarity between respective categories in a subject classificationtree, the semantic matching unit 520 determines the semantic similaritybetween the user interest subject and the service item subject. Here,the similarity graph 540 is a conversion of a hierarchical treestructure of respective categories into a graph structure connectedaccording to semantic similarity between categories in the subjectclassification tree. Each node of the similarity graph 540 representseach category of the subject classification tree, and a link betweenrespective nodes represents the existence of semantic similarity betweencorresponding categories. Further, the wPPR algorithm, which is theapplication of a weight to a Personalized PageRank algorithm widelyknown in the art, is described below in detail.

A method for generating links between respective nodes of the similaritygraph 540 is of three operations as follows. The similarity graph 540may be generated in the semantic matching unit 520, or may be generatedin a different function block of the mobile communication terminal.

Operation 1 is the operation of determining a centroid vector for eachcategory of the subject classification tree according to Equation 2below. The centroid vector, a vector being representative of learningdata of each category, is an average term vector of the learning data.Here, the learning data can be Web pages of an open directory projectused at the time of machine learning of the user data term vectorclassifier 330 or service item term vector classifier 430.

Equation 2 below represents a formula of determining a centroid vectorof each category.

$\begin{matrix}{{\overset{\rightarrow}{u}(c)} = {\frac{1}{D_{c}}{\sum\limits_{d \in D_{c}}{\overset{\rightarrow}{v}(d)}}}} & (2)\end{matrix}$

Here, the ‘c’ means a category, the ‘{right arrow over (μ)}(c)’ means acentroid vector of the category ‘c’, the ‘D_(c)’ means a learning dataset of the category ‘c’, and the ‘{right arrow over (ν)}(d)’ means aterm vector for learning data ‘d.’

Operation 2 is the operation of determining a merge centroid vector foreach category of the subject classification tree according to Equation 3below. The merge centroid vector represents the reflection of a featureof a hierarchical structure of the subject classification tree in thecentroid vectors of the respective categories. That is, the centroidvector includes only a feature of a corresponding category and does notreflect hierarchical relationship between categories within the subjectclassification tree, but the merge centroid vector represents theinclusion of features of centroid vectors of descendent categories in acentroid vector of a parent category such that the parent category caninclude features of child categories.

Equation 3 below represents a formula of determining a merge centroidvector.

$\begin{matrix}{{{\overset{\rightarrow}{\mu}}^{\prime}(c)} = {\frac{1}{1 + {{{child}(c)}}}\left( {\frac{\overset{\rightarrow}{\mu}(c)}{{\overset{\rightarrow}{\mu}(c)}} + {\sum\limits_{c_{k} \in {{child}{(c)}}}\frac{{\overset{\rightarrow}{\mu}}^{\prime}\left( c_{k} \right)}{{{\overset{\rightarrow}{\mu}}^{\prime}\left( c_{k} \right)}}}} \right)}} & (3)\end{matrix}$

Here, the ‘{right arrow over (μ)}′(c)’ means a merge centroid vector fora category ‘c’, the ‘child(c)’ means the set of child categories of thecategory ‘c’, c_(k) means a k^(th) category, and the ‘{right arrow over(μ)}(c)’ means a centroid vector for the category ‘c’.

Operation 3 is the operation of determining semantic similarity betweenall categories. The semantic similarity between the categories meanscosine similarity between merge centroid vectors for the categories.Here, the semantic matching unit 520 compares the semantic similaritybetween the categories with a threshold value. In an example where thesemantic similarity between the categories is greater than the thresholdvalue, the semantic matching unit 520 generates a link betweencorresponding categories, generating a similarity graph. At this time,as illustrated in FIG. 10, the semantic matching unit 520 sets thesemantic similarity as a weight for the generated link. Here, becausethe weight for the link is determined by the cosine similarity betweenthe merge centroid vectors, the weight for the link can represent afeature of a hierarchical structure of the subject classification tree.

The semantic matching unit 520 ranks semantic similarities of othercategories for each category in the generated similarity graph 540according to the wPPR algorithm proposed in the present disclosure.

For the sake of this, first, based on a link weight of the similaritygraph 540, the semantic matching unit 520 determines a relevance matrix(R). In the relevance matrix (R), a (i, j) component (r_(ij)) meanssemantic similarity of an i^(th) category for a j^(th) category. Thatis, the semantic matching unit 520 determines a probability that arandom surfer circulating a similarity graph makes a visit to eachcategory, using a personalized PageRank that is one of Markov RandomWalk Models widely known in the art. In a little detail, the semanticmatching unit 520 can determine a probability that the random surfermakes a visit to the i^(th) category from the j^(th) category, determinethe determined value as semantic similarity between the two categories,and rank semantic similarities of other categories for the j^(th)category according to a size of the semantic similarity.

Here, a pattern in which the random surfer circulates the similaritygraph can be defined as two examples. According to the first circulationpattern, the random surfer circulates the similarity graph at aprobability of ‘(1−d)’ every moment and, according to the secondcirculation pattern, circulates the similarity graph at a probability of‘d.’ Here, the ‘d’ is a damping factor, and can have a real number of‘0’ to ‘1’. According to experiments, an optimal value of the ‘d’ can befound empirically. In an example where the random surfer follows thefirst circulation pattern, the random surfer makes a visit to a categoryreliable within the similarity graph, i.e., a j^(th) category beingcurrently in visit in the present disclosure. In an example where therandom surfer follows the second circulation pattern, the random surfermakes a visit to a category linked with a category being currently invisit, at a probability proportional to a link weight. A probabilitythat the random surfer moves to a next category when following thesecond circulation pattern is determined according to Equation 4 below.

Equation 4 below is a formula for determining a probability of movementof a random surfer.

$\begin{matrix}{{tw}_{ij} = \frac{{sim}\left( {c_{i},c_{j}} \right)}{\sum\limits_{c_{k} \in {N{(C_{j})}}}{{sim}\left( {c_{k},c_{j}} \right)}}} & (4)\end{matrix}$

Here, the ‘tw_(ij)’ represents a probability that the random surfermoves from a j^(th) category to an i^(th) category, the ‘sim(c_(i),c_(j))’ represents similarity between the categories, i.e., a linkweight between the categories, and the ‘N(c_(j))’ represents the set ofcategories connecting with c_(j).

FIG. 11 illustrates a method for obtaining a ‘tw_(ij)’ value whenfollowing the second circulation pattern in an illustrative embodimentof the present disclosure.

Referring to FIG. 11, a probability (i.e., a ‘tw_(ij)’ value) ofmovement between categories within a subject classification tree isdetermined based on a link weight between respective categoriesillustrated in FIG. 10. For instance, assuming that a random surfercurrently makes a visit to a ‘Sports’ category 1001 and circulates asimilarity graph according to the second circulation pattern at aprobability of ‘d’, a probability of movement to each of ‘Soccer’,‘Baseball’, and ‘Shopping’ categories 1003, 1005, and 1007 linked to the‘Sports’ category 1001 is determined according to a ratio of sum(1.7=0.7 (1013)+0.7 (1015)+0.3 (1017)) of weights of the total linksconnected with the ‘Sports’ category 1001 to weight of a correspondinglink. That is, as illustrated in FIG. 11, a probability that the randomsurfer moves from the ‘Sports’ category 1001 to the ‘Soccer’ category1003 is equal to ‘0.7/1.7’ (1113), and a probability that the randomsurfer moves from the ‘Sports’ category 1001 to the ‘Baseball’ category1005 is equal to ‘0.7/1.7’ (1115), and a probability that the randomsurfer moves from the ‘Sports’ category 1001 to the ‘Shopping’ category1007 is equal to ‘0.3/1.7’ (1117).

Based on the definition of the two circulation patterns of the randomsurfer, the ‘r_(ij)’ can be determined according to Equation 5 below.

$\begin{matrix}{r_{ij} = {{d\left\lbrack {\sum\limits_{c_{k} \in {I{(c_{i})}}}{{tw}_{ik} \cdot r_{kj}}} \right\rbrack} + {\left( {1 - d} \right)t_{ij}}}} & (5)\end{matrix}$

Here, the ‘I(c_(i))’ represents the set of categories having a link to‘c_(i)’, and the ‘t_(ij)’ is for determining the first circulationpattern. In an example where a current category is set to ‘c_(j)’, the‘t_(ij)’ represents a trusted weight of the ‘c_(i)’. So, in an examplewhere ‘i’ is equal to ‘j’, the ‘t_(ij)’ is set to ‘1’ and, in remnantexamples, the ‘t_(ij)’ is set to ‘0’.

The definition of Equation 5 above using a matrix notation method can beexpressed according to Equation 6 below.R _(t) =d[W·R _(t−1)]+(1−d)T  (6)

Here, the ‘R’ represents a relevance matrix determined according to awPPR algorithm, and the ‘W’ represents a transition matrix and has thesame (i, j^(th) component as the ‘tw_(ij)’ of Equation 4 above. The ‘T’,a trusted matrix, has the same (i, j)^(th) component as the ‘t_(ij)’ ofEquation 5 above, so the ‘T’ becomes a unit matrix.

That is, the semantic matching unit 520 can digitize semantic similaritybetween arbitrary categories on the basis of the relevance matrix ofEquation 6 above. At this time, the category can be a categorycorresponding to a user interest subject or service item subject.Accordingly, in a example where the user interest subject is determinedas a category (c_(j)), the semantic matching unit 520 can determine, asa (i, j) component value of the relevance matrix, semantic similaritybetween the category (c_(i)) corresponding to the service item subjectand the category (c_(j)).

By linearly combining syntactic similarity and semantic similaritydetermined in the syntactic matching unit 510 and the semantic matchingunit 520 respectively, the integration ranking unit 530 determines thetotal similarity according to Equation 7 below.

Equation 7 below represents a formula of determining the totalsimilarity.TotalScore(u,s _(i))=(1−λ)×SyntacticScore({right arrow over (u)},{rightarrow over (s _(i))})+λ×SemanticScore(uc,sc _(i))  (7)

Here, the ‘uc’ is a category corresponding to a user interest subjectextracted from user data, and the ‘sc_(i)’ represents a categorycorresponding to a service item subject. The ‘λ’, a weight for semanticsimilarity in a linear combination, has a value of ‘0’ to ‘1.0’, and canbe determined through experiment.

The integration ranking unit 530 determines relevance ranks of serviceitems to be recommended to a user based on the total similaritydetermined through Equation 7 above.

FIGS. 13A and 13B illustrate a procedure of recommending a service itemaccording to a user interest subject in a mobile communication terminalaccording to an illustrative embodiment of the present disclosure.

Referring to FIGS. 13A and 13B, in operation 1301, the mobilecommunication terminal determines whether a preset user data collectionperiod is present. In an example where the user data collection periodis present, the mobile communication terminal proceeds to operation 1303and extracts a text from user data existing within the mobilecommunication terminal. Here, the mobile communication terminal canextract the text from the user data such as a short message, amultimedia message, an e-mail, a file, a schedule, a memo, Web-usageinformation and the like. After that, in operation 1305, the mobilecommunication terminal generates term vectors based on the extractedtext of the user data. And then, in operation 1307, the mobilecommunication terminal classifies the term vectors according to asubject classification tree embedded in the mobile communicationterminal and determines a user interest subject and then, proceeds tooperation 1317 below.

In contrast, in an example where the user data collection period is notpresent, the mobile communication terminal proceeds to operation 1309and determines whether a preset service item collection period ispresent. In an example where the service item collection period is notpresent, the mobile communication terminal returns to operation 1301 andagain performs the subsequent operations. In contrast, in an examplewhere the service item collection period is present, the mobilecommunication terminal proceeds to operation 1311 and accesses themobile Internet, collects Internet service items, and extracts a textfrom the collected service items. After that, the mobile communicationterminal proceeds to operation 1313 and generates term vectors based onthe extracted text of the service items and, in operation 1315,classifies the term vectors according to the subject classification treeand determines a service item subject and then, proceeds to operation1317 below.

In operation 1317, the mobile communication terminal determines if aservice item recommendation event takes place by a user. When theservice item recommendation event does not occur, the mobilecommunication terminal returns to operation 1301 and again performs thesubsequent operations. In contrast, when the service item recommendationevent occurs, the mobile communication terminal proceeds to operation1319 and determines syntactic similarity between a term vector for userdata and a term vector for service items. At this time, the mobilecommunication terminal can determine the syntactic similarity betweenthe user data term vector and the service item term vector, usingEquation 1 above.

After that, in operation 1321, the mobile communication terminaldetermines semantic similarity between the user interest subject and theservice item subject. Here, the mobile communication terminal connectsrespective categories in a subject classification tree according to thesemantic similarity, determines a weight for a link between therespective categories, determines a probability of movement of a randomsurfer on the basis of a general PageRank algorithm widely known in theart, and determines semantic similarity between the respectivecategories, thereby being capable of determining the semantic similaritybetween the user interest subject and the service item subject. That is,the mobile communication terminal can determine the semantic similaritybetween the user interest subject and the service item subject usingEquation 5 above.

Next, in operation 1323, the mobile communication terminal determines arelevance rank representing the total similarity between the userinterest subject and the service item subject, based on the determinedsyntactic similarity and semantic similarity. After that, in operation1325, the mobile communication terminal recommends a service itemaccording to the determined relevance rank. Here, the mobilecommunication terminal can determine the total similarity between theuser interest subject and the service item subject using Equation 7above, and can determine to have a higher relevance rank as higher isthe total similarity value of the service item subject for the userinterest subject.

After that, the mobile communication terminal terminates the algorithmaccording to the present disclosure.

Here, a description is made for collecting user data and service itemsevery constant period but, only in an example where a service itemrecommendation event takes place by a user, the mobile communicationterminal may collect the user data and the service items.

As described above, illustrative embodiments of the present disclosurehave an effect of, by determining a user interest subject from userdata, collecting Internet service items, determining a subject of theservice items, determining similarity between the user interest subjectand the service item subject, and recommending a service according tothe similarity, being capable of, even without separate input of theuser interest subject, analyzing the user interest subject based on datawithin a mobile communication terminal and recommending a relatedInternet service item in the mobile communication terminal. Further, theillustrative embodiments of the present disclosure have an effect of,because analyzing the user interest subject based on the data within themobile communication terminal, being capable of recommending a suitableInternet service item properly corresponding to the user interestsubject changing every hour. Further, the illustrative embodiments ofthe present disclosure have an effect of, instead of transmitting userperson's information to the external through a network or storing theuser person's information in a server, analyzing the user interestsubject based on the data within the mobile communication terminal andrecommending a suitable Internet service item, thereby being capable ofprotecting user person's data in the mobile communication terminal.

While the invention has been shown and described with reference tocertain preferred embodiments thereof, it will be understood by thoseskilled in the art that various changes in faint and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims.

What is claimed is:
 1. A method for providing an Internet service in amobile communication terminal, the method comprising: detecting a userinterest subject from text of user data stored within the mobilecommunication terminal; collecting service items through an access to anetwork; detecting service item subjects for the service items;determining similarities between the user interest subject and theservice item subjects; determining ranks of the service item subjects asa function of the similarities; displaying the service items accordingto the ranks of the service item subjects; and responsive to one of theservice items being selected, displaying detailed information related tothe selected service item.
 2. The method of claim 1, wherein the userdata comprises at least one of a short message, a multimedia message, anelectronic mail (e-mail), a file, a schedule, a memo, and Web-usageinformation.
 3. The method of claim 1, wherein detecting the userinterest subject from the user data comprises: extracting the text fromthe user data; analyzing the text and generating term vectors accordingto a vector space model; classifying the term vectors according to asubject classification tree; and detecting the user interest subjectaccording to a classified result.
 4. The method of claim 3, furthercomprising: storing information on the text, wherein the informationstored on the text comprises at least one of a generation time andgeneration position of the text, a kind of an application, a feature ofthe application, a revision time of the application, a generation timeof the application, and context information of the application.
 5. Themethod of claim 3, wherein the term vector reflects a weight dependenton an importance of a corresponding term within the text correspondingto the term vector.
 6. The method of claim 1, wherein detecting theservice item subject for the service items comprises: extracting a textfrom the service items; analyzing the text and generating term vectorsaccording to a vector space model; and classifying the term vectorsaccording to a subject classification tree and detecting a subject foreach of the collected service items.
 7. The method of claim 6, furthercomprising: storing information on the text, wherein the informationstored on the text comprises at least one of a generation time andgeneration position of the text, a kind of an application, a feature ofthe application, a revision time of the application, a generation timeof the application, and context information of the application.
 8. Themethod of claim 6, wherein the term vector reflects a weight dependenton an importance of a corresponding term within a text corresponding tothe term vector.
 9. The method of claim 1, wherein determining thesimilarities between the user interest subject and the service itemsubjects comprises: detecting syntactic similarity between a term vectorgenerated from the user data and a term vector generated from theservice item; detecting semantic similarity between the user interestsubject and the service item subject; and detecting a total similaritybased on the syntactic similarity and the semantic similarity, andwherein the syntactic similarity is cosine similarity between the termvector generated from the user data and the term vector generated fromthe service item.
 10. The method of claim 9, wherein determining thesemantic similarity comprises: detecting an average term vector oflearning data on respective categories in a subject classification tree;detecting merge vectors reflecting hierarchical relationships betweenthe respective categories using the average term vector; detecting alink weight between the respective categories using cosine similaritybetween the merge vectors of the respective categories; detecting aprobability of movement between the respective categories based on thelink weight; and detecting semantic similarity using the probability ofmovement between the respective categories.
 11. An apparatus forproviding an Internet service in a mobile communication terminal, theapparatus comprising: a controller configured to: detect a user interestsubject from text of user data stored within the mobile communicationterminal; collect service items through an access to a network; detectservice item subjects for the service items; determine similaritiesbetween the user interest subject and the service item subjects;determine ranks of the service item subjects based on the similarities;display the service items according to the ranks of the service itemsubjects; and responsive to one of the service items being selected,display detailed information related to the selected service item. 12.The apparatus of claim 11, wherein the user data comprises at least oneof a short message, a multimedia message, an electronic mail (e-mail), afile, a schedule, a memo, and Web-usage information.
 13. The apparatusof claim 11, wherein the controller is configured to extract the textfrom the user data, analyze the text and generates term vectorsaccording to a vector space model, and then classify the generated termvectors according to a subject classification tree and detect the userinterest subject.
 14. The apparatus of claim 13, wherein the controlleris configured to store information on the text, and wherein theinformation stored on the text comprises at least one of a generationtime and generation position of the text, a kind of an application, afeature of the application, a revision time of the application, ageneration time of the application, and context information of theapplication.
 15. The apparatus of claim 13, wherein controller isconfigured to reflect a weight dependent on an importance of acorresponding term within the text corresponding to the term vector. 16.The apparatus of claim 11, wherein the controller is configured toextract a text from the service items, analyze the text and generatesterm vectors according to a vector space model, and then classify thegenerated term vectors according to a subject classification tree anddetect a subject for each of the service items.
 17. The apparatus ofclaim 16, wherein the controller is configured to store information onthe text, and wherein the information stored on the text comprises atleast one of a generation time and generation position of the text, akind of an application, a feature of the application, a revision time ofthe application, a generation time of the application, and contextinformation of the application.
 18. The apparatus of claim 16, whereinthe controller is configured to reflect a weight dependent on animportance of a corresponding term within a text corresponding to theterm vector.
 19. The apparatus of claim 12, wherein the controller isconfigured to detect syntactic similarity between a term vectorgenerated from the user data and a term vector generated from theservice item, detect semantic similarity between the user interestsubject and the service item subject, and detect a total similaritybased on the syntactic similarity and the semantic similarity, andwherein the syntactic similarity is a cosine similarity between the termvector generated from the user data and the term vector generated fromthe service item.
 20. The apparatus of claim 19, wherein the semanticsimilarity is determined by detecting an average term vector of learningdata on respective categories in a subject classification tree,detecting merge vectors reflecting hierarchical relationships betweenthe respective categories using the average term vector, detecting alink weight between the respective categories using cosine similaritybetween the merge vectors of the respective categories, detecting aprobability of movement between the respective categories based on linkweight, and detecting a semantic similarity using the probability ofmovement between the respective categories.